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JNCI Journal of the National Cancer Institute 2006 98(8):529-534; doi:10.1093/jnci/djj131
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© The Author 2006. Published by Oxford University Press.

ARTICLE

Assessing Prostate Cancer Risk: Results from the Prostate Cancer Prevention Trial

Ian M. Thompson, Donna Pauler Ankerst, Chen Chi, Phyllis J. Goodman, Catherine M. Tangen, M. Scott Lucia, Ziding Feng, Howard L. Parnes, Charles A. Coltman, Jr.

Affiliations of authors: Department of Urology, University of Texas Health Science Center, San Antonio, TX (IMT); The Fred Hutchinson Cancer Research Center, Seattle, WA (DPA, CC, PJG, CMT, ZF); University of Colorado, Denver, CO (MSL); Division of Cancer Prevention, National Cancer Institute, Bethesda, MD (HLP); Southwest Oncology Group, San Antonio, TX (CAC)

Correspondence to: Ian Thompson, MD, Department of Urology, UTHSCSA, University of Texas HSC at San Antonio, 7703 Floyd Curl Dr., San Antonio, TX 78229 (e-mail: thompsoni{at}uthscsa.edu).

Background: Prostate-specific antigen (PSA) testing is the primary method used to diagnose prostate cancer in the United States. Methods to integrate other risk factors associated with prostate cancer into individualized risk prediction are needed. We used prostate biopsy data from men who participated in the Prostate Cancer Prevention Trial (PCPT) to develop a predictive model of prostate cancer. Methods: We included 5519 men from the placebo group of the PCPT who underwent prostate biopsy, had at least one PSA measurement and a digital rectal examination (DRE) performed during the year before the biopsy, and had at least two PSA measurements performed during the 3 years before the prostate biopsy. Logistic regression was used to model the risk of prostate cancer and high-grade disease associated with age at biopsy, race, family history of prostate cancer, PSA level, PSA velocity, DRE result, and previous prostate biopsy. Risk equations were created from the estimated logistic regression models. All statistical tests were two-sided. Results: A total of 1211 (21.9%) men were diagnosed with prostate cancer by prostate biopsy. Variables that predicted prostate cancer included higher PSA level, positive family history of prostate cancer, and abnormal DRE result, whereas a previous negative prostate biopsy was associated with reduced risk. Neither age at biopsy nor PSA velocity contributed independent prognostic information. Higher PSA level, abnormal DRE result, older age at biopsy, and African American race were predictive for high-grade disease (Gleason score ≥7) whereas a previous negative prostate biopsy reduced this risk. Conclusions: This predictive model allows an individualized assessment of prostate cancer risk and risk of high-grade disease for men who undergo a prostate biopsy.


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